Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations12444
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory12.1 MiB
Average record size in memory1016.2 B

Variable types

Numeric11
Categorical18
Text1

Alerts

Dataset has 3 (< 0.1%) duplicate rowsDuplicates
AreaType_Carpet Area is highly overall correlated with AreaType_Super Built-up AreaHigh correlation
AreaType_Super Built-up Area is highly overall correlated with AreaType_Carpet AreaHigh correlation
No_Bathroom is highly overall correlated with No_Bedroom and 3 other fieldsHigh correlation
No_Bedroom is highly overall correlated with No_Bathroom and 2 other fieldsHigh correlation
Overlooking_NA is highly overall correlated with Overlooking_park/gardenHigh correlation
Overlooking_club is highly overall correlated with Overlooking_poolHigh correlation
Overlooking_park/garden is highly overall correlated with Overlooking_NAHigh correlation
Overlooking_pool is highly overall correlated with Overlooking_clubHigh correlation
Price_in_Crore is highly overall correlated with No_Bathroom and 3 other fieldsHigh correlation
floor_number is highly overall correlated with total_floorsHigh correlation
given_area_in_sqft is highly overall correlated with No_Bathroom and 2 other fieldsHigh correlation
price_Per_Sqft_converted is highly overall correlated with Price_in_CroreHigh correlation
servant room is highly overall correlated with No_BathroomHigh correlation
total_floors is highly overall correlated with floor_numberHigh correlation
AreaType_Built-up Area is highly imbalanced (70.7%) Imbalance
Overlooking_sea facing is highly imbalanced (56.3%) Imbalance
Overlooking_lake facing is highly imbalanced (99.8%) Imbalance
Overlooking_NA is highly imbalanced (66.5%) Imbalance
Facing has 4021 (32.3%) zeros Zeros
floor_number has 276 (2.2%) zeros Zeros

Reproduction

Analysis started2024-11-05 10:40:44.019914
Analysis finished2024-11-05 10:41:28.045422
Duration44.03 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Facing
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3475571
Minimum0
Maximum7
Zeros4021
Zeros (%)32.3%
Negative0
Negative (%)0.0%
Memory size97.3 KiB
2024-11-05T10:41:28.231129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9737858
Coefficient of variation (CV)0.84078287
Kurtosis-1.2198827
Mean2.3475571
Median Absolute Deviation (MAD)2
Skewness0.12893073
Sum29213
Variance3.8958302
MonotonicityNot monotonic
2024-11-05T10:41:28.529466image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 4353
35.0%
0 4021
32.3%
2 1816
14.6%
1 754
 
6.1%
5 561
 
4.5%
3 406
 
3.3%
6 339
 
2.7%
7 194
 
1.6%
ValueCountFrequency (%)
0 4021
32.3%
1 754
 
6.1%
2 1816
14.6%
3 406
 
3.3%
4 4353
35.0%
5 561
 
4.5%
6 339
 
2.7%
7 194
 
1.6%
ValueCountFrequency (%)
7 194
 
1.6%
6 339
 
2.7%
5 561
 
4.5%
4 4353
35.0%
3 406
 
3.3%
2 1816
14.6%
1 754
 
6.1%
0 4021
32.3%

No_Bedroom
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0699936
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.3 KiB
2024-11-05T10:41:29.103616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.74207407
Coefficient of variation (CV)0.24171844
Kurtosis0.32977096
Mean3.0699936
Median Absolute Deviation (MAD)0
Skewness0.16482178
Sum38203
Variance0.55067392
MonotonicityNot monotonic
2024-11-05T10:41:29.347111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 6787
54.5%
4 2903
23.3%
2 2376
 
19.1%
5 246
 
2.0%
1 109
 
0.9%
6 22
 
0.2%
7 1
 
< 0.1%
ValueCountFrequency (%)
1 109
 
0.9%
2 2376
 
19.1%
3 6787
54.5%
4 2903
23.3%
5 246
 
2.0%
6 22
 
0.2%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 22
 
0.2%
5 246
 
2.0%
4 2903
23.3%
3 6787
54.5%
2 2376
 
19.1%
1 109
 
0.9%

No_Bathroom
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3445034
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.3 KiB
2024-11-05T10:41:29.594463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0826978
Coefficient of variation (CV)0.32372453
Kurtosis0.49847956
Mean3.3445034
Median Absolute Deviation (MAD)1
Skewness0.60882987
Sum41619
Variance1.1722345
MonotonicityNot monotonic
2024-11-05T10:41:29.862495image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 4584
36.8%
4 3446
27.7%
2 2671
21.5%
5 1157
 
9.3%
6 376
 
3.0%
1 131
 
1.1%
7 64
 
0.5%
8 14
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
1 131
 
1.1%
2 2671
21.5%
3 4584
36.8%
4 3446
27.7%
5 1157
 
9.3%
6 376
 
3.0%
7 64
 
0.5%
8 14
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 14
 
0.1%
7 64
 
0.5%
6 376
 
3.0%
5 1157
 
9.3%
4 3446
27.7%
3 4584
36.8%
2 2671
21.5%
1 131
 
1.1%

No_Balcony
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
4
4947 
3
4353 
2
2547 
1
583 
0
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row4

Common Values

ValueCountFrequency (%)
4 4947
39.8%
3 4353
35.0%
2 2547
20.5%
1 583
 
4.7%
0 14
 
0.1%

Length

2024-11-05T10:41:30.179668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:30.451556image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
4 4947
39.8%
3 4353
35.0%
2 2547
20.5%
1 583
 
4.7%
0 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
4 4947
39.8%
3 4353
35.0%
2 2547
20.5%
1 583
 
4.7%
0 14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4947
39.8%
3 4353
35.0%
2 2547
20.5%
1 583
 
4.7%
0 14
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4947
39.8%
3 4353
35.0%
2 2547
20.5%
1 583
 
4.7%
0 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4947
39.8%
3 4353
35.0%
2 2547
20.5%
1 583
 
4.7%
0 14
 
0.1%

Corner_Property
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
1
8207 
0
4237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8207
66.0%
0 4237
34.0%

Length

2024-11-05T10:41:30.731854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:30.961975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 8207
66.0%
0 4237
34.0%

Most occurring characters

ValueCountFrequency (%)
1 8207
66.0%
0 4237
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8207
66.0%
0 4237
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8207
66.0%
0 4237
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8207
66.0%
0 4237
34.0%

Furnishing
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size729.3 KiB
2.0
9399 
3.0
2157 
1.0
 
884
0.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37332
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 9399
75.5%
3.0 2157
 
17.3%
1.0 884
 
7.1%
0.0 4
 
< 0.1%

Length

2024-11-05T10:41:31.218863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:31.487573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 9399
75.5%
3.0 2157
 
17.3%
1.0 884
 
7.1%
0.0 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12448
33.3%
. 12444
33.3%
2 9399
25.2%
3 2157
 
5.8%
1 884
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12448
33.3%
. 12444
33.3%
2 9399
25.2%
3 2157
 
5.8%
1 884
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12448
33.3%
. 12444
33.3%
2 9399
25.2%
3 2157
 
5.8%
1 884
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12448
33.3%
. 12444
33.3%
2 9399
25.2%
3 2157
 
5.8%
1 884
 
2.4%

Price_in_Crore
Real number (ℝ)

High correlation 

Distinct989
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4454637
Minimum0.16
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.3 KiB
2024-11-05T10:41:31.785375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.16
5-th percentile0.8
Q11.75
median2.47
Q33.92
95-th percentile8.5
Maximum85
Range84.84
Interquartile range (IQR)2.17

Descriptive statistics

Standard deviation4.0861379
Coefficient of variation (CV)1.1859472
Kurtosis113.61755
Mean3.4454637
Median Absolute Deviation (MAD)0.97
Skewness8.6083188
Sum42875.35
Variance16.696523
MonotonicityNot monotonic
2024-11-05T10:41:32.190068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.25 197
 
1.6%
2.1 181
 
1.5%
3.5 180
 
1.4%
1.8 179
 
1.4%
2.5 178
 
1.4%
2.3 175
 
1.4%
1.85 173
 
1.4%
1.9 166
 
1.3%
2.4 164
 
1.3%
2 161
 
1.3%
Other values (979) 10690
85.9%
ValueCountFrequency (%)
0.16 1
 
< 0.1%
0.18 1
 
< 0.1%
0.22 1
 
< 0.1%
0.25 2
< 0.1%
0.26 1
 
< 0.1%
0.27 1
 
< 0.1%
0.28 2
< 0.1%
0.285 1
 
< 0.1%
0.29 3
< 0.1%
0.3 4
< 0.1%
ValueCountFrequency (%)
85 1
 
< 0.1%
75 2
 
< 0.1%
74 1
 
< 0.1%
70 2
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
66.5 1
 
< 0.1%
66 1
 
< 0.1%
65 2
 
< 0.1%
60 6
< 0.1%

price_Per_Sqft_converted
Real number (ℝ)

High correlation 

Distinct6857
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32196.086
Minimum113
Maximum3673527.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.3 KiB
2024-11-05T10:41:32.563331image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum113
5-th percentile7600
Q110541
median13529
Q317842
95-th percentile29500
Maximum3673527.2
Range3673414.2
Interquartile range (IQR)7301

Descriptive statistics

Standard deviation182428.86
Coefficient of variation (CV)5.6661813
Kurtosis184.1412
Mean32196.086
Median Absolute Deviation (MAD)3477.5
Skewness12.851088
Sum4.0064809 × 108
Variance3.3280289 × 1010
MonotonicityNot monotonic
2024-11-05T10:41:32.952688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 64
 
0.5%
10000 64
 
0.5%
12000 57
 
0.5%
15000 47
 
0.4%
12500 42
 
0.3%
16000 37
 
0.3%
16666 37
 
0.3%
14000 36
 
0.3%
17000 36
 
0.3%
11111 35
 
0.3%
Other values (6847) 11989
96.3%
ValueCountFrequency (%)
113 1
< 0.1%
978 1
< 0.1%
1850 1
< 0.1%
2909 1
< 0.1%
3000 1
< 0.1%
3562 1
< 0.1%
3753 1
< 0.1%
4000 1
< 0.1%
4024 1
< 0.1%
4166 1
< 0.1%
ValueCountFrequency (%)
3673527.156 1
< 0.1%
3526577.028 1
< 0.1%
3519128.34 1
< 0.1%
3345128.28 1
< 0.1%
3136048.344 2
< 0.1%
3102012.576 1
< 0.1%
3047880.42 1
< 0.1%
3041658.828 2
< 0.1%
3031519.14 1
< 0.1%
3012822.072 1
< 0.1%

given_area_in_sqft
Real number (ℝ)

High correlation 

Distinct1580
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2201.9491
Minimum250
Maximum59115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.3 KiB
2024-11-05T10:41:33.394572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile903
Q11550
median1950
Q32505
95-th percentile4038
Maximum59115
Range58865
Interquartile range (IQR)955

Descriptive statistics

Standard deviation1389.8942
Coefficient of variation (CV)0.63121088
Kurtosis287.7175
Mean2201.9491
Median Absolute Deviation (MAD)460
Skewness10.309111
Sum27401055
Variance1931806
MonotonicityNot monotonic
2024-11-05T10:41:33.784795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400 202
 
1.6%
1950 136
 
1.1%
1650 128
 
1.0%
2150 124
 
1.0%
2000 103
 
0.8%
1850 96
 
0.8%
1930 92
 
0.7%
2450 88
 
0.7%
1800 84
 
0.7%
1750 78
 
0.6%
Other values (1570) 11313
90.9%
ValueCountFrequency (%)
250 1
 
< 0.1%
300 1
 
< 0.1%
302 4
< 0.1%
308 6
< 0.1%
320 1
 
< 0.1%
321 1
 
< 0.1%
339 1
 
< 0.1%
342 2
 
< 0.1%
350 3
< 0.1%
351 4
< 0.1%
ValueCountFrequency (%)
59115 1
 
< 0.1%
34982 1
 
< 0.1%
29277 1
 
< 0.1%
20000 1
 
< 0.1%
19967 3
< 0.1%
19913 1
 
< 0.1%
19687 2
< 0.1%
19350 1
 
< 0.1%
17405 1
 
< 0.1%
15134 2
< 0.1%

floor_number
Real number (ℝ)

High correlation  Zeros 

Distinct50
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3961749
Minimum-2
Maximum50
Zeros276
Zeros (%)2.2%
Negative22
Negative (%)0.2%
Memory size97.3 KiB
2024-11-05T10:41:34.154209image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile1
Q15
median8
Q312
95-th percentile22
Maximum50
Range52
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.4801632
Coefficient of variation (CV)0.6896597
Kurtosis1.9198564
Mean9.3961749
Median Absolute Deviation (MAD)4
Skewness1.1600622
Sum116926
Variance41.992514
MonotonicityNot monotonic
2024-11-05T10:41:34.565471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1030
 
8.3%
7 939
 
7.5%
5 912
 
7.3%
8 905
 
7.3%
6 804
 
6.5%
9 737
 
5.9%
2 721
 
5.8%
4 712
 
5.7%
3 694
 
5.6%
12 660
 
5.3%
Other values (40) 4330
34.8%
ValueCountFrequency (%)
-2 4
 
< 0.1%
-1 18
 
0.1%
0 276
 
2.2%
1 473
3.8%
2 721
5.8%
3 694
5.6%
4 712
5.7%
5 912
7.3%
6 804
6.5%
7 939
7.5%
ValueCountFrequency (%)
50 1
 
< 0.1%
49 1
 
< 0.1%
48 1
 
< 0.1%
47 1
 
< 0.1%
45 3
< 0.1%
42 1
 
< 0.1%
41 2
 
< 0.1%
40 5
< 0.1%
39 4
< 0.1%
38 2
 
< 0.1%

total_floors
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.011009
Minimum0
Maximum60
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size97.3 KiB
2024-11-05T10:41:34.918530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q114
median19
Q329
95-th percentile39
Maximum60
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.9808766
Coefficient of variation (CV)0.4750308
Kurtosis-0.34178555
Mean21.011009
Median Absolute Deviation (MAD)6
Skewness0.34295173
Sum261461
Variance99.617897
MonotonicityNot monotonic
2024-11-05T10:41:35.330914image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 1577
 
12.7%
19 770
 
6.2%
4 725
 
5.8%
18 669
 
5.4%
32 551
 
4.4%
12 500
 
4.0%
29 493
 
4.0%
24 483
 
3.9%
17 469
 
3.8%
30 424
 
3.4%
Other values (44) 5783
46.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 100
 
0.8%
3 96
 
0.8%
4 725
5.8%
5 41
 
0.3%
6 9
 
0.1%
7 47
 
0.4%
8 75
 
0.6%
9 127
 
1.0%
ValueCountFrequency (%)
60 2
 
< 0.1%
59 1
 
< 0.1%
55 1
 
< 0.1%
51 45
0.4%
49 1
 
< 0.1%
48 12
 
0.1%
47 61
0.5%
46 3
 
< 0.1%
45 18
 
0.1%
44 2
 
< 0.1%

AreaType_Built-up Area
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
0
11801 
1
 
643

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11801
94.8%
1 643
 
5.2%

Length

2024-11-05T10:41:36.059328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:36.553111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 11801
94.8%
1 643
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 11801
94.8%
1 643
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11801
94.8%
1 643
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11801
94.8%
1 643
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11801
94.8%
1 643
 
5.2%

AreaType_Carpet Area
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
0
10596 
1
1848 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10596
85.1%
1 1848
 
14.9%

Length

2024-11-05T10:41:37.120666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:37.684157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 10596
85.1%
1 1848
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 10596
85.1%
1 1848
 
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10596
85.1%
1 1848
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10596
85.1%
1 1848
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10596
85.1%
1 1848
 
14.9%

AreaType_Super Built-up Area
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
1
9953 
0
2491 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9953
80.0%
0 2491
 
20.0%

Length

2024-11-05T10:41:38.337491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:38.861377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 9953
80.0%
0 2491
 
20.0%

Most occurring characters

ValueCountFrequency (%)
1 9953
80.0%
0 2491
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 9953
80.0%
0 2491
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 9953
80.0%
0 2491
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 9953
80.0%
0 2491
 
20.0%

study room
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
0
9480 
1
2964 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9480
76.2%
1 2964
 
23.8%

Length

2024-11-05T10:41:39.553542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:40.331694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 9480
76.2%
1 2964
 
23.8%

Most occurring characters

ValueCountFrequency (%)
0 9480
76.2%
1 2964
 
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9480
76.2%
1 2964
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9480
76.2%
1 2964
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9480
76.2%
1 2964
 
23.8%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
1
6461 
0
5983 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 6461
51.9%
0 5983
48.1%

Length

2024-11-05T10:41:41.021873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:41.304376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 6461
51.9%
0 5983
48.1%

Most occurring characters

ValueCountFrequency (%)
1 6461
51.9%
0 5983
48.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6461
51.9%
0 5983
48.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6461
51.9%
0 5983
48.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6461
51.9%
0 5983
48.1%

store room
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
0
11021 
1
1423 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11021
88.6%
1 1423
 
11.4%

Length

2024-11-05T10:41:41.568519image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:41.805600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 11021
88.6%
1 1423
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 11021
88.6%
1 1423
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11021
88.6%
1 1423
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11021
88.6%
1 1423
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11021
88.6%
1 1423
 
11.4%

pooja room
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
0
9621 
1
2823 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9621
77.3%
1 2823
 
22.7%

Length

2024-11-05T10:41:42.096316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:42.336351image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 9621
77.3%
1 2823
 
22.7%

Most occurring characters

ValueCountFrequency (%)
0 9621
77.3%
1 2823
 
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9621
77.3%
1 2823
 
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9621
77.3%
1 2823
 
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9621
77.3%
1 2823
 
22.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
0
7218 
1
5226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7218
58.0%
1 5226
42.0%

Length

2024-11-05T10:41:42.592595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:42.839047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 7218
58.0%
1 5226
42.0%

Most occurring characters

ValueCountFrequency (%)
0 7218
58.0%
1 5226
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7218
58.0%
1 5226
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7218
58.0%
1 5226
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7218
58.0%
1 5226
42.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
1
8481 
0
3963 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 8481
68.2%
0 3963
31.8%

Length

2024-11-05T10:41:43.114051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:43.336114image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 8481
68.2%
0 3963
31.8%

Most occurring characters

ValueCountFrequency (%)
1 8481
68.2%
0 3963
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8481
68.2%
0 3963
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8481
68.2%
0 3963
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8481
68.2%
0 3963
31.8%

Overlooking_sea facing
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
0
11321 
1
 
1123

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11321
91.0%
1 1123
 
9.0%

Length

2024-11-05T10:41:43.582442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:43.827540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 11321
91.0%
1 1123
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 11321
91.0%
1 1123
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11321
91.0%
1 1123
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11321
91.0%
1 1123
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11321
91.0%
1 1123
 
9.0%

Overlooking_club
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
1
8321 
0
4123 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8321
66.9%
0 4123
33.1%

Length

2024-11-05T10:41:44.106421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:44.330693image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 8321
66.9%
0 4123
33.1%

Most occurring characters

ValueCountFrequency (%)
1 8321
66.9%
0 4123
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8321
66.9%
0 4123
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8321
66.9%
0 4123
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8321
66.9%
0 4123
33.1%

Overlooking_park/garden
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
1
10506 
0
1938 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10506
84.4%
0 1938
 
15.6%

Length

2024-11-05T10:41:44.588490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:44.818514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 10506
84.4%
0 1938
 
15.6%

Most occurring characters

ValueCountFrequency (%)
1 10506
84.4%
0 1938
 
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10506
84.4%
0 1938
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10506
84.4%
0 1938
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10506
84.4%
0 1938
 
15.6%

Overlooking_pool
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
1
8108 
0
4336 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8108
65.2%
0 4336
34.8%

Length

2024-11-05T10:41:45.075671image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:45.310073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 8108
65.2%
0 4336
34.8%

Most occurring characters

ValueCountFrequency (%)
1 8108
65.2%
0 4336
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8108
65.2%
0 4336
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8108
65.2%
0 4336
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8108
65.2%
0 4336
34.8%

Overlooking_lake facing
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
0
12442 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12442
> 99.9%
1 2
 
< 0.1%

Length

2024-11-05T10:41:45.567527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:45.805057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 12442
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12442
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12442
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12442
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12442
> 99.9%
1 2
 
< 0.1%

Overlooking_NA
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
0
11672 
1
 
772

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12444
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11672
93.8%
1 772
 
6.2%

Length

2024-11-05T10:41:46.566134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T10:41:46.791264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 11672
93.8%
1 772
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 11672
93.8%
1 772
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11672
93.8%
1 772
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11672
93.8%
1 772
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11672
93.8%
1 772
 
6.2%

flat_age
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7767599
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.3 KiB
2024-11-05T10:41:47.009104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1143652
Coefficient of variation (CV)0.40131853
Kurtosis-0.56822589
Mean2.7767599
Median Absolute Deviation (MAD)1
Skewness-0.078829378
Sum34554
Variance1.2418098
MonotonicityNot monotonic
2024-11-05T10:41:47.285274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 5443
43.7%
1 2270
18.2%
4 2124
 
17.1%
2 1860
 
14.9%
5 743
 
6.0%
6 4
 
< 0.1%
ValueCountFrequency (%)
1 2270
18.2%
2 1860
 
14.9%
3 5443
43.7%
4 2124
 
17.1%
5 743
 
6.0%
6 4
 
< 0.1%
ValueCountFrequency (%)
6 4
 
< 0.1%
5 743
 
6.0%
4 2124
 
17.1%
3 5443
43.7%
2 1860
 
14.9%
1 2270
18.2%

sector_num
Real number (ℝ)

Distinct89
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.685712
Minimum1
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.3 KiB
2024-11-05T10:41:47.607648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33
Q156
median71
Q393
95-th percentile110
Maximum113
Range112
Interquartile range (IQR)37

Descriptive statistics

Standard deviation24.991909
Coefficient of variation (CV)0.33916899
Kurtosis-0.54070964
Mean73.685712
Median Absolute Deviation (MAD)18
Skewness-0.29378002
Sum916945
Variance624.5955
MonotonicityNot monotonic
2024-11-05T10:41:47.939385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 576
 
4.6%
37 560
 
4.5%
65 475
 
3.8%
70 416
 
3.3%
67 412
 
3.3%
47 326
 
2.6%
106 325
 
2.6%
113 323
 
2.6%
69 306
 
2.5%
104 299
 
2.4%
Other values (79) 8426
67.7%
ValueCountFrequency (%)
1 11
 
0.1%
2 95
0.8%
3 29
 
0.2%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
9 5
 
< 0.1%
15 2
 
< 0.1%
18 2
 
< 0.1%
22 21
 
0.2%
ValueCountFrequency (%)
113 323
2.6%
112 77
 
0.6%
111 217
1.7%
110 95
 
0.8%
109 202
1.6%
108 255
2.0%
107 160
1.3%
106 325
2.6%
105 1
 
< 0.1%
104 299
2.4%

luxury_score
Real number (ℝ)

Distinct178
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.55681
Minimum0
Maximum189
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size97.3 KiB
2024-11-05T10:41:48.274061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42
Q180
median118
Q3164
95-th percentile189
Maximum189
Range189
Interquartile range (IQR)84

Descriptive statistics

Standard deviation46.919071
Coefficient of variation (CV)0.39244163
Kurtosis-1.1451114
Mean119.55681
Median Absolute Deviation (MAD)40.5
Skewness-0.054026202
Sum1487765
Variance2201.3992
MonotonicityNot monotonic
2024-11-05T10:41:48.633727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189 895
 
7.2%
182 694
 
5.6%
42 271
 
2.2%
77 181
 
1.5%
83 179
 
1.4%
186 158
 
1.3%
91 157
 
1.3%
71 155
 
1.2%
106 154
 
1.2%
78 148
 
1.2%
Other values (168) 9452
76.0%
ValueCountFrequency (%)
0 9
0.1%
6 2
 
< 0.1%
9 7
0.1%
10 1
 
< 0.1%
11 2
 
< 0.1%
12 3
 
< 0.1%
15 1
 
< 0.1%
16 4
 
< 0.1%
17 11
0.1%
18 14
0.1%
ValueCountFrequency (%)
189 895
7.2%
186 158
 
1.3%
185 27
 
0.2%
184 50
 
0.4%
183 44
 
0.4%
182 694
5.6%
181 87
 
0.7%
180 71
 
0.6%
179 119
 
1.0%
178 49
 
0.4%
Distinct11490
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size9.3 MiB
2024-11-05T10:41:49.202174image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5428
Median length1479
Mean length697.92768
Min length78

Characters and Unicode

Total characters8685012
Distinct characters98
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11017 ?
Unique (%)88.5%

Sample

1st rowGreen court society, which is bang on 60-Meter road and near to dwarka expressway, so overall connectivity is good. The society is surrounded by luxury dlf projects. It has lots of parking space (First come basis) and ample green areas. The infrastructure is well maintained and floor layouts are good. 100% power backup is available. ashiana homes green court ['Baba Kanala Chowk', 'Pataudi Rd', 'Gurukul Preschool', 'Yaduvanshi Shiksha Niketan', 'Bharat Ram Global School', 'RPS International School', 'ICICI ATM', 'Silver Streak Hospital', 'Arc Multi Speciality', 'Sanjeevani Hospital', 'HDFC Bank', 'Sai Sports Club cricket ground', 'Nawada Cricket Accadmy', 'HP PETROL PUMP Unnamed Rd', 'INOX Cinema']
2nd rowGreen court society, which is bang on 60-Meter road and near to dwarka expressway, so overall connectivity is good. The society is surrounded by luxury dlf projects. It has lots of parking space (First come basis) and ample green areas. The infrastructure is well maintained and floor layouts are good. 100% power backup is available. ashiana homes green court ['Baba Kanala Chowk', 'Pataudi Rd', 'Gurukul Preschool', 'Yaduvanshi Shiksha Niketan', 'Bharat Ram Global School', 'RPS International School', 'ICICI ATM', 'Silver Streak Hospital', 'Arc Multi Speciality', 'Sanjeevani Hospital', 'HDFC Bank', 'Sai Sports Club cricket ground', 'Nawada Cricket Accadmy', 'HP PETROL PUMP Unnamed Rd', 'INOX Cinema']
3rd rowAvailable for sale 2 bhk semi furnished in pyramid urban homes sector 70a gurgaon full wood work in all bedrooms modular kitchen walking to market mall etc dbn group pyramid urban homes ['Airia Mall', 'Sohna Road', "St. Angel's Global, Sector 70 A", 'Sparsh Hospital', 'Indira Gandhi International Airport', 'Universal Business Park', 'Lemon Tree Hotel, Sohna Road', 'De Adventure Park', 'SkyJumper Trampoline Park']
4th rowLooking for a 2 bhk property for sale in gurgaon? Buy this 2 bhk flat in m3m the marina that is situated in sector 68 gurgaon. This residential flat is east-Facing direction. Constructed on a super built up area of 1260 sq.Ft., the flat comprises 2 bedroom(s), 2 bathrooms and 2 balconies. The flat has a total of 20 floors and this property is situated on 9th floor. Being a ready to move project, you can expect immediate possession of this 0-1 year old property. The floor of this flat is beautifully designed using wood flooring, giving the flat an alluring look. The flat will offer a modern lifestyle as it is presented with many of the amenities such as swimming pool, club house / community center, cctv surveillance, fitness centre / gym, park, lift(s), maintenance staff and visitor parking. The residential project is well equipped to meet all your water needs through access to municipal corporation and borewell/tank supply. shree shyam properties m3m the marina ['Airia Mall', 'Southern Peripheral Road', 'Sohna Road', 'Alpine Convent\xa0School', 'MKD Hospital', 'Indira Gandhi International Airport', 'Bhondsi Nature Park', 'Lemon Tree Hotel, Sohna Road', 'De Adventure Park', 'PVR Drive in Theatre']
5th rowThis 2 bhk apartment is available for sale in m3m woodshire, one of the most prominent projects for flats in sector 107 gurgaon. The flat is north-Facing. The floor plan additionally contains 2 bedroom(s), 2 bathrooms and more than 3 balconies. All in all, the flat is spread over a super built up area of 1366 sq.Ft. The flat has a total of 14 floors and this property is situated on 1st floor. This 1-5 years old property is available for immediate possession as the project is ready to move. The spartex flooring of this flat is beautifully designed and helps to give it a pleasing look. The flat will offer a modern lifestyle as it is presented with many of the amenities such as swimming pool, security personnel, maintenance staff, shopping centre, club house / community center, cctv surveillance, fitness centre / gym, park and lift(s). Municipal corporation provides a regular supply of water to this residential project. jbm buildtech pvt ltd m3m woodshire ['Signum 107', 'Nora Solomon Medicenter', 'Indira Gandhi International Airport', 'The Shikshiyan School', 'Najafgarh Jheel Bird Sanctuary', 'Skylark Cricket Academy']
ValueCountFrequency (%)
the 39084
 
3.0%
is 26527
 
2.1%
of 25482
 
2.0%
gurgaon 24880
 
1.9%
in 23056
 
1.8%
this 20939
 
1.6%
flat 20208
 
1.6%
and 19796
 
1.5%
a 19414
 
1.5%
sector 17539
 
1.4%
Other values (11888) 1045622
81.5%
2024-11-05T10:41:50.132946image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1266961
14.6%
a 655053
 
7.5%
e 534707
 
6.2%
t 491743
 
5.7%
o 489220
 
5.6%
i 477948
 
5.5%
r 474552
 
5.5%
n 436591
 
5.0%
s 365998
 
4.2%
' 345186
 
4.0%
Other values (88) 3147053
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8685012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1266961
14.6%
a 655053
 
7.5%
e 534707
 
6.2%
t 491743
 
5.7%
o 489220
 
5.6%
i 477948
 
5.5%
r 474552
 
5.5%
n 436591
 
5.0%
s 365998
 
4.2%
' 345186
 
4.0%
Other values (88) 3147053
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8685012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1266961
14.6%
a 655053
 
7.5%
e 534707
 
6.2%
t 491743
 
5.7%
o 489220
 
5.6%
i 477948
 
5.5%
r 474552
 
5.5%
n 436591
 
5.0%
s 365998
 
4.2%
' 345186
 
4.0%
Other values (88) 3147053
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8685012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1266961
14.6%
a 655053
 
7.5%
e 534707
 
6.2%
t 491743
 
5.7%
o 489220
 
5.6%
i 477948
 
5.5%
r 474552
 
5.5%
n 436591
 
5.0%
s 365998
 
4.2%
' 345186
 
4.0%
Other values (88) 3147053
36.2%

Interactions

2024-11-05T10:41:22.508563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:50.824366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:55.083954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:58.446481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:01.373243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:03.975076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:07.022736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:10.734391image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:13.606864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:16.666358image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:19.412321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:22.889555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:51.440058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:55.458318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:58.889719image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:01.615495image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:04.227068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:07.323557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:11.112788image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:13.839430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:16.905062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:19.661509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:23.578097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:51.770070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:55.750672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:59.128026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:01.855918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:04.478224image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:07.717940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:11.463066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:14.082727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:17.156296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:19.911334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:23.973385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:52.148212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:56.098526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:59.383632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:02.108243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:04.741047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:08.061049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:11.703779image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:14.387008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:17.417366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:20.187912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:24.552056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:52.455557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:56.463334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:59.634401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:02.352123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:04.998316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:08.311302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:11.919305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:14.605496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:17.666582image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:20.400390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:24.964757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:52.877646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:56.913109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:59.895859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:02.599913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:05.277839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:08.679873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:12.178166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:14.865771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:17.914757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:20.672182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:25.283340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:53.233069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:57.270679image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:00.144824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:02.841674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:05.748756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:09.042397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:12.434191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:15.124090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:18.144583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:20.923043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:25.512221image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:53.609685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:57.487603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:00.383328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:03.055513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:05.997460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:09.408518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:12.657961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:15.361305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-05T10:41:21.160460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:25.756098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:54.004748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:57.722624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:00.630080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:03.288683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-05T10:41:15.886354image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:18.678811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:21.430268image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:26.001323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:54.323315image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:57.952643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:00.878419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:03.502357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:06.500018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:10.072358image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:13.102445image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:16.134489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:18.910633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-05T10:41:26.259366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:40:54.696823image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-05T10:41:01.104975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-05T10:41:06.754869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:10.382910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:13.361450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-05T10:41:19.148052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-05T10:41:22.106379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-05T10:41:50.473899image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AreaType_Built-up AreaAreaType_Carpet AreaAreaType_Super Built-up AreaCorner_PropertyFacingFurnishingNo_BalconyNo_BathroomNo_BedroomOverlooking_NAOverlooking_clubOverlooking_lake facingOverlooking_main roadOverlooking_othersOverlooking_park/gardenOverlooking_poolOverlooking_sea facingPrice_in_Croreflat_agefloor_numbergiven_area_in_sqftluxury_scorepooja roomprice_Per_Sqft_convertedsector_numservant roomstore roomstudy roomtotal_floors
AreaType_Built-up Area1.0000.0970.4660.0290.0660.0070.0310.0410.0410.0090.0050.0000.0080.0000.0240.0230.0380.0100.0840.0640.0000.0440.1010.0000.0550.0160.0620.0900.122
AreaType_Carpet Area0.0971.0000.8340.1080.0270.0380.1700.1640.1380.1160.1150.0000.0250.0210.0770.1110.0000.0000.0940.1800.0180.2070.0110.2070.0690.1240.0000.0210.238
AreaType_Super Built-up Area0.4660.8341.0000.0790.0160.0250.1410.1560.1150.0950.0960.0000.0290.0150.0530.0840.0220.0070.1200.1800.0050.1780.0420.1780.0720.1200.0360.0700.265
Corner_Property0.0290.1080.0791.0000.0660.0360.0870.0380.0290.3060.3130.0060.3100.2060.3160.3180.1260.0130.0540.0460.0000.3140.0790.0000.0780.0570.0450.0500.045
Facing0.0660.0270.0160.0661.0000.0480.0460.010-0.0120.0890.1430.0000.1200.0880.0780.0880.084-0.0380.0710.011-0.0170.0280.090-0.039-0.0090.0550.0830.075-0.038
Furnishing0.0070.0380.0250.0360.0481.0000.0530.0880.0810.1100.0830.0000.0400.0520.0950.0760.0160.0330.0600.0360.0000.0790.0420.0230.1090.1000.0150.0710.070
No_Balcony0.0310.1700.1410.0870.0460.0531.0000.2630.2510.0670.1370.0170.0380.0410.0710.1590.0280.0520.0650.0710.0390.0990.0900.0580.0760.3330.0500.0780.118
No_Bathroom0.0410.1640.1560.0380.0100.0880.2631.0000.8300.0610.1030.0000.0390.0650.0550.1130.0170.6620.1730.1510.7980.1000.1500.231-0.0490.6370.0550.1110.129
No_Bedroom0.0410.1380.1150.029-0.0120.0810.2510.8301.0000.0590.1020.0140.0340.0700.0510.1160.0240.6590.1440.1360.7950.0720.1550.228-0.0790.4930.0910.0880.109
Overlooking_NA0.0090.1160.0950.3060.0890.1100.0670.0610.0591.0000.3650.0000.3760.2180.5980.3510.0800.0470.0390.0250.0290.3710.0690.0760.0320.0590.0240.0190.038
Overlooking_club0.0050.1150.0960.3130.1430.0830.1370.1030.1020.3651.0000.0070.4810.2900.4620.7150.1560.0330.1690.0930.0220.3050.1160.0350.1520.1100.0440.0780.200
Overlooking_lake facing0.0000.0000.0000.0060.0000.0000.0170.0000.0140.0000.0071.0000.0080.0000.0000.0060.0000.0000.0000.0060.0000.0250.0000.0000.0000.0000.0240.0000.000
Overlooking_main road0.0080.0250.0290.3100.1200.0400.0380.0390.0340.3760.4810.0081.0000.3910.3270.4350.2000.0630.1240.0180.0000.2190.0960.0240.1300.0100.0480.0550.066
Overlooking_others0.0000.0210.0150.2060.0880.0520.0410.0650.0700.2180.2900.0000.3911.0000.1710.2930.2920.0260.0880.0340.0090.2660.1120.0220.0610.0180.0630.0370.037
Overlooking_park/garden0.0240.0770.0530.3160.0780.0950.0710.0550.0510.5980.4620.0000.3270.1711.0000.4800.1210.0260.0490.0340.0250.2790.0940.0510.0560.0710.0370.0420.063
Overlooking_pool0.0230.1110.0840.3180.0880.0760.1590.1130.1160.3510.7150.0060.4350.2930.4801.0000.1680.0500.1630.0880.0260.3030.1100.0370.1160.1200.0500.0820.179
Overlooking_sea facing0.0380.0000.0220.1260.0840.0160.0280.0170.0240.0800.1560.0000.2000.2920.1210.1681.0000.0240.0730.0420.0000.2390.0100.0060.0610.0100.1090.0000.052
Price_in_Crore0.0100.0000.0070.013-0.0380.0330.0520.6620.6590.0470.0330.0000.0630.0260.0260.0500.0241.0000.1100.2840.8200.1340.1630.729-0.1950.1700.1200.1160.403
flat_age0.0840.0940.1200.0540.0710.0600.0650.1730.1440.0390.1690.0000.1240.0880.0490.1630.0730.1101.000-0.0640.1380.0030.0560.029-0.1920.1530.0580.176-0.231
floor_number0.0640.1800.1800.0460.0110.0360.0710.1510.1360.0250.0930.0060.0180.0340.0340.0880.0420.284-0.0641.0000.2270.0820.0740.2050.0780.1450.0610.0950.567
given_area_in_sqft0.0000.0180.0050.000-0.0170.0000.0390.7980.7950.0290.0220.0000.0000.0090.0250.0260.0000.8200.1380.2271.0000.1270.1170.330-0.0880.0900.0950.0920.273
luxury_score0.0440.2070.1780.3140.0280.0790.0990.1000.0720.3710.3050.0250.2190.2660.2790.3030.2390.1340.0030.0820.1271.0000.2380.0900.0190.1510.2140.1030.112
pooja room0.1010.0110.0420.0790.0900.0420.0900.1500.1550.0690.1160.0000.0960.1120.0940.1100.0100.1630.0560.0740.1170.2381.0000.0160.0670.1160.2600.2370.080
price_Per_Sqft_converted0.0000.2070.1780.000-0.0390.0230.0580.2310.2280.0760.0350.0000.0240.0220.0510.0370.0060.7290.0290.2050.3300.0900.0161.000-0.2430.0710.0540.0150.358
sector_num0.0550.0690.0720.078-0.0090.1090.076-0.049-0.0790.0320.1520.0000.1300.0610.0560.1160.061-0.195-0.1920.078-0.0880.0190.067-0.2431.0000.1590.0920.0830.143
servant room0.0160.1240.1200.0570.0550.1000.3330.6370.4930.0590.1100.0000.0100.0180.0710.1200.0100.1700.1530.1450.0900.1510.1160.0710.1591.0000.0700.0340.215
store room0.0620.0000.0360.0450.0830.0150.0500.0550.0910.0240.0440.0240.0480.0630.0370.0500.1090.1200.0580.0610.0950.2140.2600.0540.0920.0701.0000.1900.067
study room0.0900.0210.0700.0500.0750.0710.0780.1110.0880.0190.0780.0000.0550.0370.0420.0820.0000.1160.1760.0950.0920.1030.2370.0150.0830.0340.1901.0000.166
total_floors0.1220.2380.2650.045-0.0380.0700.1180.1290.1090.0380.2000.0000.0660.0370.0630.1790.0520.403-0.2310.5670.2730.1120.0800.3580.1430.2150.0670.1661.000

Missing values

2024-11-05T10:41:26.675581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-05T10:41:27.560910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

FacingNo_BedroomNo_BathroomNo_BalconyCorner_PropertyFurnishingPrice_in_Croreprice_Per_Sqft_convertedgiven_area_in_sqftfloor_numbertotal_floorsAreaType_Built-up AreaAreaType_Carpet AreaAreaType_Super Built-up Areastudy roomservant roomstore roompooja roomOverlooking_othersOverlooking_main roadOverlooking_sea facingOverlooking_clubOverlooking_park/gardenOverlooking_poolOverlooking_lake facingOverlooking_NAflat_agesector_numluxury_scoreText_info
01.022.0103.00.618840.0690141400100101100100039088Green court society, which is bang on 60-Meter road and near to dwarka expressway, so overall connectivity is good. The society is surrounded by luxury dlf projects. It has lots of parking space (First come basis) and ample green areas. The infrastructure is well maintained and floor layouts are good. 100% power backup is available. ashiana homes green court ['Baba Kanala Chowk', 'Pataudi Rd', 'Gurukul Preschool', 'Yaduvanshi Shiksha Niketan', 'Bharat Ram Global School', 'RPS International School', 'ICICI ATM', 'Silver Streak Hospital', 'Arc Multi Speciality', 'Sanjeevani Hospital', 'HDFC Bank', 'Sai Sports Club cricket ground', 'Nawada Cricket Accadmy', 'HP PETROL PUMP Unnamed Rd', 'INOX Cinema']
13.022.0103.00.7010144.069081400100101100100039083Green court society, which is bang on 60-Meter road and near to dwarka expressway, so overall connectivity is good. The society is surrounded by luxury dlf projects. It has lots of parking space (First come basis) and ample green areas. The infrastructure is well maintained and floor layouts are good. 100% power backup is available. ashiana homes green court ['Baba Kanala Chowk', 'Pataudi Rd', 'Gurukul Preschool', 'Yaduvanshi Shiksha Niketan', 'Bharat Ram Global School', 'RPS International School', 'ICICI ATM', 'Silver Streak Hospital', 'Arc Multi Speciality', 'Sanjeevani Hospital', 'HDFC Bank', 'Sai Sports Club cricket ground', 'Nawada Cricket Accadmy', 'HP PETROL PUMP Unnamed Rd', 'INOX Cinema']
24.022.0112.00.718492.083691700100000101100037099Available for sale 2 bhk semi furnished in pyramid urban homes sector 70a gurgaon full wood work in all bedrooms modular kitchen walking to market mall etc dbn group pyramid urban homes ['Airia Mall', 'Sohna Road', "St. Angel's Global, Sector 70 A", 'Sparsh Hospital', 'Indira Gandhi International Airport', 'Universal Business Park', 'Lemon Tree Hotel, Sohna Road', 'De Adventure Park', 'SkyJumper Trampoline Park']
30.022.0212.01.5512301.0126092000100000101110016873Looking for a 2 bhk property for sale in gurgaon? Buy this 2 bhk flat in m3m the marina that is situated in sector 68 gurgaon. This residential flat is east-Facing direction. Constructed on a super built up area of 1260 sq.Ft., the flat comprises 2 bedroom(s), 2 bathrooms and 2 balconies. The flat has a total of 20 floors and this property is situated on 9th floor. Being a ready to move project, you can expect immediate possession of this 0-1 year old property. The floor of this flat is beautifully designed using wood flooring, giving the flat an alluring look. The flat will offer a modern lifestyle as it is presented with many of the amenities such as swimming pool, club house / community center, cctv surveillance, fitness centre / gym, park, lift(s), maintenance staff and visitor parking. The residential project is well equipped to meet all your water needs through access to municipal corporation and borewell/tank supply. shree shyam properties m3m the marina ['Airia Mall', 'Southern Peripheral Road', 'Sohna Road', 'Alpine Convent\xa0School', 'MKD Hospital', 'Indira Gandhi International Airport', 'Bhondsi Nature Park', 'Lemon Tree Hotel, Sohna Road', 'De Adventure Park', 'PVR Drive in Theatre']
42.022.0411.01.309516.01366114001000000011100310781This 2 bhk apartment is available for sale in m3m woodshire, one of the most prominent projects for flats in sector 107 gurgaon. The flat is north-Facing. The floor plan additionally contains 2 bedroom(s), 2 bathrooms and more than 3 balconies. All in all, the flat is spread over a super built up area of 1366 sq.Ft. The flat has a total of 14 floors and this property is situated on 1st floor. This 1-5 years old property is available for immediate possession as the project is ready to move. The spartex flooring of this flat is beautifully designed and helps to give it a pleasing look. The flat will offer a modern lifestyle as it is presented with many of the amenities such as swimming pool, security personnel, maintenance staff, shopping centre, club house / community center, cctv surveillance, fitness centre / gym, park and lift(s). Municipal corporation provides a regular supply of water to this residential project. jbm buildtech pvt ltd m3m woodshire ['Signum 107', 'Nora Solomon Medicenter', 'Indira Gandhi International Airport', 'The Shikshiyan School', 'Najafgarh Jheel Bird Sanctuary', 'Skylark Cricket Academy']
52.022.0112.00.519286.054971401000000100100039538Rof ananda is one of gurgaon's most sought after destination for apartments and this 2 bhk flat in sector 95 gurgaon is your opportunity to be a part of this community. The flat is north-East-Facing. The flat is over 549 sq.Ft. Carpet area and comes with 2 bedroom(s), 2 bathrooms and 1 balcony. This flat lies on the 7th level of a 14 storey building. This 1-5 years old property is available for immediate possession as the project is ready to move. The well built marble flooring enhances the aesthetic appeal of this flat. All the modern amenities such as grocery shop, fitness centre / gym, park, lift(s) and maintenance staff will make life easier for you. This residential project ensures a 24*7 water supply for its residents. philby real estate rof ananda ['Metro', 'Dwarka Expressway', 'Rajeev Chowk', 'NH8', 'KMP Expressway', 'IMT Manesar', 'ISBT', 'Hero Honda Chowk', 'IGI Airport', 'Railway Station', 'Proposed Diplomatic enclave', 'Flava', 'Spicy Salsa', "Nihar's Cafe", 'Cheeni singh restaurant']
61.022.0212.00.599164.064341901000010100100039550This lovely 2 bhk apartment/flat in sector 95a gurgaon is available for sale in one of gurgaon's most popular projects, signature the roselia. This property faces the north-West direction. The flat occupies a carpet area of 644 sq.Ft. That consists of 2 bedrooms, 2 bathrooms and 2 balconies. The property is located on the 4th floor of a 19 floors tall building. An added advantage of this 1-5 years old flat is that it is available for immediate possession as the project is already ready to move. The beautifully designed marble flooring enhances the beauty of the flat. Many of the modern amenities being offered, like swimming pool, park, lift(s), maintenance staff and visitor parking, will provide a pleasant living experience for you. This residential project ensures a 24*7 water supply for its residents. philby real estate signature the roselia ['Sector 37 Metro Station', 'Newtown Square Mall', 'Pataudi Road', 'Yaduvanshi Shiksha Niketan', 'Gurugram University Sector 87', 'Arc Multi Speciality hospital', 'Indira Gandhi International Airport', 'Basai Dhankot Railway Station', 'IMT Manesar', 'Holiday Inn Sector 90', 'Manesar Golf Course', 'Nakhrola Stadium']
70.023.0412.01.7010173.016712426001000101001000385179This 2 bhk flat is located in ss the leaf, which houses some of the most spacious flats in sector 85 gurgaon. This is a east-Facing property. The flat is over 1671 sq.Ft. Super built up area and comes with 2 bedroom(s), 3 bathrooms and more than 3 balconies. The property is located on the 24th floor of a 26 floors tall building. An added advantage of this 1-5 years old flat is that it is available for immediate possession as the project is already ready to move. The well built marble flooring enhances the aesthetic appeal of this flat. The society complex is in the close vicinity of close to school, close to hospital and close to market, making it an ideal home for a relaxed lifestyle. The flat will offer a modern lifestyle as it is presented with many of the amenities such as swimming pool, security personnel, maintenance staff, shopping centre, club house / community center, cctv surveillance, fitness centre / gym, park, lift(s), visitor parking and water softening plant. The residential project is well equipped to meet all your water needs through access to borewell/tank supply. eternity home exchange ss the leaf ['Sapphire 83 Mall', 'Dwarka Expressway', 'Central Peripheral Road', 'NH 08', 'Pataudi Road', 'Delhi Public School Sector 84', 'DPG Institute of Technology', 'Genesis Hospital Sector 84', 'Indira Gandhi International Airport', 'Imt Manesar', 'Holiday Inn Hotel Sector 90', 'SkyJumper Trampoline Park', 'Nakhrola Stadium Sector 81A']
81.023.0212.01.649814.01671626001000101001000385165Situated in sector 85 gurgaon, ss the leaf is a well planned society that offers a pleasant living experience to its residents. This 2 bhk flat in gurgaon is your opportunity to be a part of this community. This is a west-Facing property. Containing 2 bedroom(s), 3 bathrooms and 2 balconies, this flat is spread over a super built up area of 1671 sq.Ft. The residential building has 26 floors in total and the flat for sale is located on the 6th floor. As the project is already ready to move, so you can easily move into this 1-5 years old property. The marble flooring of this flat is beautifully designed and helps to give it a pleasing look. This residential property is situated near close to school, close to market and close to hospital. Ss the leaf is designed very well to provide modern facilities such as swimming pool, security personnel, maintenance staff, shopping centre, club house / community center, cctv surveillance, fitness centre / gym, park, lift(s), visitor parking and water softening plant. The project provides access to clean water through borewell/tank supply. eternity home exchange ss the leaf ['Sapphire 83 Mall', 'Dwarka Expressway', 'Central Peripheral Road', 'NH 08', 'Pataudi Road', 'Delhi Public School Sector 84', 'DPG Institute of Technology', 'Genesis Hospital Sector 84', 'Indira Gandhi International Airport', 'Imt Manesar', 'Holiday Inn Hotel Sector 90', 'SkyJumper Trampoline Park', 'Nakhrola Stadium Sector 81A']
91.022.0212.01.7517156.0102041200110001111110036544Emaar mgf emerald estate is one of the most popular destination for buying apartments/ flats in sector 65 gurgaon. You too can be a part of this society by purchasing this 2 bhk flat here. The flat is facing the west direction. The floor plan additionally contains 2 bedroom(s), 2 bathrooms and 2 balconies. All in all, the flat is spread over a super built up area of 1020 sq.Ft. This flat is situated on the 3rd floor of this 12 floors tall residential building. As the project is already ready to move, so you can easily move into this 1-5 years old property. The floor of this flat is beautifully designed using vitrified flooring, giving the flat an alluring look. The flat will offer a modern lifestyle as it is presented with many of the amenities such as swimming pool, grocery shop, shopping centre, club house / community center, fitness centre / gym, park, lift(s), maintenance staff and visitor parking. The housing society ensures a continuous supply of water to your flat from municipal corporation and borewell/tank. jmd properties emaar mgf emerald estate ['Sector 53-54 Metro Station', 'Central Plaza Mall', 'Golf Course Road', 'NH 148A', 'Lancers International School', 'Paras Hospitals, Gurgaon', 'Indira Gandhi Intl Airport', 'DLF Golf and Country Club']
FacingNo_BedroomNo_BathroomNo_BalconyCorner_PropertyFurnishingPrice_in_Croreprice_Per_Sqft_convertedgiven_area_in_sqftfloor_numbertotal_floorsAreaType_Built-up AreaAreaType_Carpet AreaAreaType_Super Built-up Areastudy roomservant roomstore roompooja roomOverlooking_othersOverlooking_main roadOverlooking_sea facingOverlooking_clubOverlooking_park/gardenOverlooking_poolOverlooking_lake facingOverlooking_NAflat_agesector_numluxury_scoreText_info
124344.033.0212.02.389916.02400025010010001011100399178Assotech blith is one of gurgaon's most sought after destination for apartments and this 3 bhk flat in sector 99 gurgaon is your opportunity to be a part of this community. The flat is facing the north-East direction. Containing 3 bedroom(s), 3 bathrooms and 2 balconies, this flat is spread over a c... vinod associates assotech blith ['Ocus Medley Mall', 'Dwarka Expressway', 'Suncity School', 'SGT University', 'The Signature Advanced Super Speciality', 'Indira Gandhi International Airport', 'Basai Dhankot Railway Station', 'Skyview Corporate Park', 'Park Inn, Gurgaon', 'Tau DeviLal Sports Complex']
124350.033.0212.02.4010000.02400725010011001011000399178This beautiful 3 bhk flat in sector 99 gurgaon is situated in assotech blith, one of the popular residential society in gurgaon. This property faces the east direction. The floor plan additionally contains 3 bedroom(s), 3 bathrooms and 2 balconies. All in all, the flat is spread over a carpet area o... vinod associates assotech blith ['Ocus Medley Mall', 'Dwarka Expressway', 'Suncity School', 'SGT University', 'The Signature Advanced Super Speciality', 'Indira Gandhi International Airport', 'Basai Dhankot Railway Station', 'Skyview Corporate Park', 'Park Inn, Gurgaon', 'Tau DeviLal Sports Complex']
124360.034.0412.03.4018888.02660141900111011101110038199Bestech park view grand spa is one of gurgaon's most sought after destination for apartments and this 3 bhk flat in sector 81 gurgaon is your opportunity to be a part of this community. This property faces the east direction. The floor plan additionally contains 3 bedroom(s), 4 bathrooms and more th... gre solutions bestech park view grand spa ['Sapphire 83 Mall', 'NH-8, IMT Manesar', 'Dwarka Expressway', "St. Xavier's High School", 'Spectra Hospital', 'Indira Gandhi International Airport', 'IMT Manesar', 'Hyatt Regency Gurgaon', 'SkyJumper Trampoline Park', 'National Tennis Academy']
124372.045.0201.04.2517081.02488714001010000001100367101Bestech park view spa next is one of gurgaon's most sought after destination for apartments and this 4 bhk flat in sector 67 gurgaon is your opportunity to be a part of this community. This property faces the north direction. Containing 4 bedroom(s), 5 bathrooms and 2 balconies, this flat is spread ... mg homes bestech park view spa next ['Omaxe City Centre Mall', 'Golf Course Extension Road', "St. Xavier's School", 'KIIT College of Engineering', 'Ektaa Hospital', 'Indira Gandhi International Airport', 'Surajgarh Gurgaon', 'Duke Horse Riding Club', 'Radisson Hotel Gurugram', 'SkyJumper Trampoline Park', 'HUDA Mini Golf Course', 'PVR Drive In Theater']
124380.046.0411.015.8041578.03800217001010100001000348180This 4 bhk flat is located in central park resorts, which houses some of the most spacious flats in sector 48 gurgaon. The flat is east-Facing. The floor plan additionally contains 4 bedroom(s), 6 bathrooms and more than 3 balconies. All in all, the flat is spread over a super built up area of 3800 ... radiance realtors central park resorts ['Huda Metro Station', 'Omaxe Celebration Mall', 'Iffco Chowk', 'GD Goenka Public School', 'Cambridge College Of Education', 'Gurugram University', 'Park Hospital', 'Indira Gandhi Int. Airport', 'Basai Dhankot']
124395.034.0202.01.608938.01790218001010001011100382102Mapsko royal ville apartment in sector 82, gurgaon is a ready-To-Move property. It offers apartment in varied budget range. These units are a perfect combination of comfort and style, specifically designed to suit your requirements and conveniences. There are 3bhk apartments and 3bhk apartment avail... gre solutions mapsko royale ville ['Sapphire 83 Mall', 'Golf Course Ext Rd', "St. Xavier's High School", 'DPG Institute of Technology', 'Miracles Apollo Cradle', 'Indira Gandhi International Airport', 'Imt Manesar', 'Holiday Inn Gurugram Sector 90', 'SkyJumper Trampoline Park', 'Vishalgarh Farms', 'F9 Go Karting Gurgaon']
124402.033.0212.02.9016066.01805414001000011011100449144This 3 bhk flat is located in orchid petals, which houses some of the most spacious flats in sector 49 gurgaon. This is a north-Facing property. Containing 3 bedroom(s), 3 bathrooms and 2 balconies, this flat is spread over a super built up area of 1805 sq.Ft. The residential building has 14 floors ... paras associates & developers orchid petals ['Sapphire Mall', 'Omaxe City Centre', 'BigBazaar', 'Sohna Rd', 'Kinder Care Playschool', 'Kangaroo Kids Preschool', 'shiv Mandir', 'Polaris Hospital', 'Medanta Hospital', 'Artemis Hospital', 'HDFC Bank', 'Radisson Hotel', 'SRS Cinemas']
124412.034.0412.02.2511255.019991035001010001011100368189This is a resale unit 3bhk + study + servant with all work done like :-Ac (5), fan (5), lights, wardrobe (4), modular kitchen with chimney and hobb also this is back facing + sun facing unit.Note we have a multiple options available in this society we are the oldest agent of this society so, i ha... ganganiya realtors pareena mi casa ['Sector 55-56 Metro Station', 'Airia Mall', 'NH 48', 'The Vivekananda School - Sector 69', 'Gurugram University', 'Park Hospital', 'Indira Gandhi International Airport', 'Gurgaon Railway Station', 'Spaze Corporate Park', 'Golden Greens Golf & Resorts Limited', 'Tau DeviLal Sports Complex']
124422.045.0412.03.8018765.02383914001010000011000449152Situated in block s sector 49 gurgaon, bestech park view city 2 is a well planned society that offers a pleasant living experience to its residents. This 4 bhk flat in gurgaon is your opportunity to be a part of this community. The flat occupies a super built up area of 2383 sq.Ft. That consists of ... paras associates & developers bestech park view city 2 ['Sector 55-56 Metro Station', 'Raheja Mall', 'Hong Kong Bazaar', 'Sohna Road', 'Delhi Jaipur Expressway', 'DAV Public School', 'Gurugram University', 'W Pratiksha Hospital', 'Indira Gandhi International Airport', 'Bestech Business Tower', 'Radisson Hotel Gurugram Sohna Road', 'HUDA Mini Golf Course', 'SkyJumper Trampoline Park Gurgaon', 'SCC Drive-In Cinema', 'Duke Horse Riding Club']
124435.044.0412.06.5027083.0281082000101000101110044376Looking for a 4 bhk property for sale in gurgaon? Buy this 4 bhk flat in dlf westend heights that is situated in dlf phase 5, gurgaon. The flat is north-West-Facing. Constructed on a super built up area of 2810 sq.Ft., the flat comprises 4 bedroom(s), 4 bathrooms and more than 3 balconies. This flat... gurgaon homes dlf westend heights ['Sector 53-54 metro station', 'Sector 42-43 metro station', 'The shopping mall', 'Hanuman Mandir', 'New Life Church', 'Hdfc ATM', 'Rbs ATM', 'Standard chartered ATM', 'Hdfc bank ATM', 'Citi bank ATM', 'Icici bank ATM', 'Paras Hospital Gurgaon', 'Arihant Hospital', 'Gupta', 'Marwah Clinic', 'The Dental Lounge', 'Guardian Pharmacy', 'Chikitsa', 'Bharat petroleum', 'Heera Fuel Station', 'HCG CNG Station', 'Hdfc bank & atm', 'Axis bank', 'Icici bank', 'Hdfc bank', 'Kotak mahindra bank', 'Indusind bank', 'Icici bank', 'Hdfc bank', 'Cafe Tonini', 'Sagar Ratna', "Carl's Jr.", 'Shophouse by Kylin', 'Starbucks', 'Clock tower', 'PWO house', 'Balaji Vegetarian Paradise', 'Naivedyam Restaurant', 'Burger Singh', 'The Chicken Boat', 'Wat-a-Burger', 'Bikanerwala', 'Stones2milestones', 'IILM', 'Iilm University', 'Two Horizon Center', 'Mantis Eye LLP', 'PK Online', 'Dawnbit', 'Ncr library']

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FacingNo_BedroomNo_BathroomNo_BalconyCorner_PropertyFurnishingPrice_in_Croreprice_Per_Sqft_convertedgiven_area_in_sqftfloor_numbertotal_floorsAreaType_Built-up AreaAreaType_Carpet AreaAreaType_Super Built-up Areastudy roomservant roomstore roompooja roomOverlooking_othersOverlooking_main roadOverlooking_sea facingOverlooking_clubOverlooking_park/gardenOverlooking_poolOverlooking_lake facingOverlooking_NAflat_agesector_numluxury_scoreText_info# duplicates
02.022.0213.01.309923.01310813001000011011100391114Its fantastic design, ultimate green space, open to sky, word class modern amenities, superb designed by developer, finest club house in the vicinity. Top of the world style living g prop tarc maceo ['Manish Gallexie 91', 'Dwarka Expressway', 'Rao Bharat Singh International School', 'Dronacharya College of Engineering', 'Silver Streak Multi Speciality Hospital', 'Indira Gandhi International Airport', 'IMT Manesar', 'Holiday Inn Sector 90', 'Manesar Golf Course', 'National Tennis Academy']2
12.022.0302.01.809146.01640162600100000000000138570Spacious rooms in a prime society of new gurgaon. Easy access to all important places. smarthomes realtors ss the leaf ['Sapphire 83 Mall', 'Dwarka Expressway', 'Central Peripheral Road', 'NH 08', 'Pataudi Road', 'Delhi Public School Sector 84', 'DPG Institute of Technology', 'Genesis Hospital Sector 84', 'Indira Gandhi International Airport', 'Imt Manesar', 'Holiday Inn Hotel Sector 90', 'SkyJumper Trampoline Park', 'Nakhrola Stadium Sector 81A']2
24.044.0412.04.2817469.0245028350011101111111002104182This 4 bhk apartment is available for sale in hero homes, one of the most prominent projects for flats in sector 104 gurgaon. Containing 4 bedroom(s), 4 bathrooms and more than 3 balconies, this flat is spread over a super built up area of 2450 sq.Ft. The residential building has 35 floors in total ... aastha estates hero homes ['Iffco Chowk Metro Station', 'Conscient One', 'Dwarka Expressway', 'Euro International School', 'The NorthCap University', 'Aryan Hospital', 'Indira Gandhi International Airport', 'Gurgaon Railway Station', 'Country Inn & Suites by Radisson', 'AapnoGhar Amusement Park', 'Hamoni Golf Camp', 'Tau DeviLal Sports Complex']2